Generation of precision preclinical cancer models using regulated in vivo base editing.
Journal
Nature biotechnology
ISSN: 1546-1696
Titre abrégé: Nat Biotechnol
Pays: United States
ID NLM: 9604648
Informations de publication
Date de publication:
Mar 2024
Mar 2024
Historique:
received:
14
07
2022
accepted:
10
07
2023
medline:
18
3
2024
pubmed:
11
8
2023
entrez:
10
8
2023
Statut:
ppublish
Résumé
Although single-nucleotide variants (SNVs) make up the majority of cancer-associated genetic changes and have been comprehensively catalogued, little is known about their impact on tumor initiation and progression. To enable the functional interrogation of cancer-associated SNVs, we developed a mouse system for temporal and regulatable in vivo base editing. The inducible base editing (iBE) mouse carries a single expression-optimized cytosine base editor transgene under the control of a tetracycline response element and enables robust, doxycycline-dependent expression across a broad range of tissues in vivo. Combined with plasmid-based or synthetic guide RNAs, iBE drives efficient engineering of individual or multiple SNVs in intestinal, lung and pancreatic organoids. Temporal regulation of base editor activity allows controlled sequential genome editing ex vivo and in vivo, and delivery of sgRNAs directly to target tissues facilitates generation of in situ preclinical cancer models.
Identifiants
pubmed: 37563300
doi: 10.1038/s41587-023-01900-x
pii: 10.1038/s41587-023-01900-x
doi:
Substances chimiques
RNA, Guide, CRISPR-Cas Systems
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
437-447Subventions
Organisme : NCI NIH HHS
ID : R01 CA233944
Pays : United States
Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.
Références
Goodwin, S., McPherson, J. D. & McCombie, W. R. Coming of age: ten years of next-generation sequencing technologies. Nat. Rev. Genet. 17, 333–351 (2016).
pubmed: 27184599
pmcid: 10373632
doi: 10.1038/nrg.2016.49
Landrum, M. J. et al. ClinVar: public archive of interpretations of clinically relevant variants. Nucleic Acids Res. 44, D862–D868 (2016).
pubmed: 26582918
doi: 10.1093/nar/gkv1222
Vogelstein, B. et al. Cancer genome landscapes. Science 339, 1546–1558 (2013).
pubmed: 23539594
pmcid: 3749880
doi: 10.1126/science.1235122
Vivanco, I. et al. Differential sensitivity of glioma- versus lung cancer-specific EGFR mutations to EGFR kinase inhibitors. Cancer Discov. 2, 458–471 (2012).
pubmed: 22588883
pmcid: 3354723
doi: 10.1158/2159-8290.CD-11-0284
Hyman, D. M. et al. AKT inhibition in solid tumors with AKT1 mutations. J. Clin. Oncol. 35, 2251–2259 (2017).
pubmed: 28489509
pmcid: 5501365
doi: 10.1200/JCO.2017.73.0143
Vasan, N. et al. Double PIK3CA mutations in cis increase oncogenicity and sensitivity to PI3Kα inhibitors. Science 366, 714–723 (2019).
pubmed: 31699932
pmcid: 7173400
doi: 10.1126/science.aaw9032
Findlay, G. M. et al. Accurate classification of BRCA1 variants with saturation genome editing. Nature 562, 217–222 (2018).
pubmed: 30209399
pmcid: 6181777
doi: 10.1038/s41586-018-0461-z
Zafra, M. P. et al. Optimized base editors enable efficient editing in cells, organoids and mice. Nat. Biotechnol. 36, 888–893 (2018).
pubmed: 29969439
pmcid: 6130889
doi: 10.1038/nbt.4194
Komor, A. C., Kim, Y. B., Packer, M. S., Zuris, J. A. & Liu, D. R. Programmable editing of a target base in genomic DNA without double-stranded DNA cleavage. Nature 533, 420–424 (2016).
pubmed: 27096365
pmcid: 4873371
doi: 10.1038/nature17946
Gaudelli, N. M. et al. Programmable base editing of A•T to G•C in genomic DNA without DNA cleavage. Nature 551, 464–471 (2017).
pubmed: 29160308
pmcid: 5726555
doi: 10.1038/nature24644
Gaudelli, N. M. et al. Directed evolution of adenine base editors with increased activity and therapeutic application. Nat. Biotechnol. 38, 892–900 (2020).
pubmed: 32284586
doi: 10.1038/s41587-020-0491-6
Komor, A. C. et al. Improved base excision repair inhibition and bacteriophage Mu Gam protein yields C:G-to-T:A base editors with higher efficiency and product purity. Sci. Adv. 3, eaao4774 (2017).
pubmed: 28875174
pmcid: 5576876
doi: 10.1126/sciadv.aao4774
Rothgangl, T. et al. In vivo adenine base editing of PCSK9 in macaques reduces LDL cholesterol levels. Nat. Biotechnol. 39, 949–957 (2021).
pubmed: 34012094
pmcid: 8352781
doi: 10.1038/s41587-021-00933-4
Villiger, L. et al. In vivo cytidine base editing of hepatocytes without detectable off-target mutations in RNA and DNA. Nat. Biomed. Eng. 5, 179–189 (2021).
pubmed: 33495639
pmcid: 7610981
doi: 10.1038/s41551-020-00671-z
Villiger, L. et al. Treatment of a metabolic liver disease by in vivo genome base editing in adult mice. Nat. Med. 24, 1519–1525 (2018).
pubmed: 30297904
doi: 10.1038/s41591-018-0209-1
Song, C.-Q. et al. Adenine base editing in an adult mouse model of tyrosinaemia. Nat. Biomed. Eng. 4, 125–130 (2019).
Yeh, W. H., Chiang, H., Rees, H. A., Edge, A. S. B. & Liu, D. R. In vivo base editing of post-mitotic sensory cells. Nat. Commun. 9, 2184 (2018).
pubmed: 29872041
pmcid: 5988727
doi: 10.1038/s41467-018-04580-3
Banskota, S. et al. Engineered virus-like particles for efficient in vivo delivery of therapeutic proteins. Cell 185, 250–265 (2022).
pubmed: 35021064
pmcid: 8809250
doi: 10.1016/j.cell.2021.12.021
Ryu, S. M. et al. Adenine base editing in mouse embryos and an adult mouse model of Duchenne muscular dystrophy. Nat. Biotechnol. 36, 536–539 (2018).
pubmed: 29702637
doi: 10.1038/nbt.4148
Yang, L. et al. Amelioration of an inherited metabolic liver disease through creation of a de novo start codon by cytidine base editing. Mol. Ther. 28, 1673–1683 (2020).
pubmed: 32413280
pmcid: 7335753
doi: 10.1016/j.ymthe.2020.05.001
Dow, L. E. et al. Conditional reverse tet-transactivator mouse strains for the efficient induction of TRE-regulated transgenes in mice. PLoS ONE 9, e95236 (2014).
pubmed: 24743474
pmcid: 3990578
doi: 10.1371/journal.pone.0095236
Premsrirut, P. K. et al. A rapid and scalable system for studying gene function in mice using conditional RNA interference. Cell 145, 145–158 (2011).
pubmed: 21458673
pmcid: 3244080
doi: 10.1016/j.cell.2011.03.012
Grunewald, J. et al. CRISPR DNA base editors with reduced RNA off-target and self-editing activities. Nat. Biotechnol. 37, 1041–1048 (2019).
pubmed: 31477922
pmcid: 6730565
doi: 10.1038/s41587-019-0236-6
Yan, N. et al. Cytosine base editors induce off-target mutations and adverse phenotypic effects in transgenic mice. Nat. Commun. 14, 1784 (2023).
pubmed: 36997536
pmcid: 10063651
doi: 10.1038/s41467-023-37508-7
Zehir, A. et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat. Med. 23, 703–713 (2017).
pubmed: 28481359
pmcid: 5461196
doi: 10.1038/nm.4333
Zafra, M. P. et al. An in vivo Kras allelic series reveals distinct phenotypes of common oncogenic variants. Cancer Discov. 10, 1654–1671 (2020).
pubmed: 32792368
pmcid: 7642097
doi: 10.1158/2159-8290.CD-20-0442
Schatoff, E. M. et al. Distinct CRC-associated APC mutations dictate response to tankyrase inhibition. Cancer Discov. 9, 1358–1371 (2019).
Katti, A. et al. GO: a functional reporter system to identify and enrich base editing activity. Nucleic Acids Res. 48, 2841–2852 (2020).
pubmed: 32112097
pmcid: 7102966
doi: 10.1093/nar/gkaa124
Sanchez-Rivera, F. J. et al. Base editing sensor libraries for high-throughput engineering and functional analysis of cancer-associated single nucleotide variants. Nat. Biotechnol. 40, 862–873 (2022).
pubmed: 35165384
pmcid: 9232935
doi: 10.1038/s41587-021-01172-3
Mehta, A. & Merkel, O. M. Immunogenicity of Cas9 protein. J. Pharm. Sci. 109, 62–67 (2020).
pubmed: 31589876
doi: 10.1016/j.xphs.2019.10.003
Chew, W. L. et al. A multifunctional AAV–CRISPR–Cas9 and its host response. Nat. Methods 13, 868–874 (2016).
pubmed: 27595405
pmcid: 5374744
doi: 10.1038/nmeth.3993
Wang, D. et al. Adenovirus-mediated somatic genome editing of Pten by CRISPR/Cas9 in mouse liver in spite of Cas9-specific immune responses. Hum. Gene Ther. 26, 432–442 (2015).
pubmed: 26086867
pmcid: 4509492
doi: 10.1089/hum.2015.087
Ruiz de Galarreta, M. et al. β-catenin activation promotes immune escape and resistance to anti-PD-1 therapy in hepatocellular carcinoma. Cancer Discov. 9, 1124–1141 (2019).
Calvisi, D. F. et al. Activation of the canonical Wnt/β-catenin pathway confers growth advantages in c-Myc/E2F1 transgenic mouse model of liver cancer. J. Hepatol. 42, 842–849 (2005).
pubmed: 15885355
doi: 10.1016/j.jhep.2005.01.029
Hingorani, S. R. et al. Trp53R172H and KrasG12D cooperate to promote chromosomal instability and widely metastatic pancreatic ductal adenocarcinoma in mice. Cancer Cell 7, 469–483 (2005).
pubmed: 15894267
doi: 10.1016/j.ccr.2005.04.023
Alsner, J. et al. A comparison between p53 accumulation determined by immunohistochemistry and TP53 mutations as prognostic variables in tumours from breast cancer patients. Acta Oncol. 47, 600–607 (2008).
pubmed: 18465328
doi: 10.1080/02841860802047411
Freed-Pastor, W. A. & Prives, C. Mutant p53: one name, many proteins. Genes Dev. 26, 1268–1286 (2012).
pubmed: 22713868
pmcid: 3387655
doi: 10.1101/gad.190678.112
Bartek, J., Iggo, R., Gannon, J. & Lane, D. P. Genetic and immunochemical analysis of mutant p53 in human breast cancer cell lines. Oncogene 5, 893–899 (1990).
pubmed: 1694291
Maresch, R. et al. Multiplexed pancreatic genome engineering and cancer induction by transfection-based CRISPR/Cas9 delivery in mice. Nat. Commun. 7, 10770 (2016).
pubmed: 26916719
pmcid: 4773438
doi: 10.1038/ncomms10770
Park, J. S. et al. Pancreatic cancer induced by in vivo electroporation-enhanced sleeping beauty transposon gene delivery system in mouse. Pancreas 43, 614–618 (2014).
pubmed: 24713671
doi: 10.1097/MPA.0000000000000102
Annunziato, S. et al. In situ CRISPR–Cas9 base editing for the development of genetically engineered mouse models of breast cancer. EMBO J. 39, e102169 (2020).
Zhou, C. et al. Off-target RNA mutation induced by DNA base editing and its elimination by mutagenesis. Nature 571, 275–278 (2019).
pubmed: 31181567
doi: 10.1038/s41586-019-1314-0
Arbab, M. et al. Determinants of base editing outcomes from target library analysis and machine learning. Cell 182, 463–480 (2020).
pubmed: 32533916
pmcid: 7384975
doi: 10.1016/j.cell.2020.05.037
Marquart, K. F. et al. Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens. Nat. Commun. 12, 5114 (2021).
pubmed: 34433819
pmcid: 8387386
doi: 10.1038/s41467-021-25375-z
Pallaseni, A. et al. Predicting base editing outcomes using position-specific sequence determinants. Nucleic Acids Res. 50, 3551–3564 (2022).
pubmed: 35286377
pmcid: 8989541
doi: 10.1093/nar/gkac161
Park, J. & Kim, H. K. Prediction of base editing efficiencies and outcomes using DeepABE and DeepCBE. Methods Mol. Biol. 2606, 23–32 (2023).
pubmed: 36592305
doi: 10.1007/978-1-0716-2879-9_3
Kim, Y. et al. High-throughput functional evaluation of human cancer-associated mutations using base editors. Nat. Biotechnol. 40, 874–884 (2022).
pubmed: 35411116
pmcid: 10243181
doi: 10.1038/s41587-022-01276-4
Winters, I. P. et al. Multiplexed in vivo homology-directed repair and tumor barcoding enables parallel quantification of Kras variant oncogenicity. Nat. Commun. 8, 2053 (2017).
pubmed: 29233960
pmcid: 5727199
doi: 10.1038/s41467-017-01519-y
Bock, D. et al. In vivo prime editing of a metabolic liver disease in mice. Sci. Transl. Med. 14, eabl9238 (2022).
pubmed: 35294257
pmcid: 7614134
doi: 10.1126/scitranslmed.abl9238
Davis, J. R. et al. Efficient prime editing in mouse brain, liver and heart with dual AAVs. Nat. Biotechnol. https://doi.org/10.1038/s41587-023-01758-z (2023).
Dow, L. E. et al. A pipeline for the generation of shRNA transgenic mice. Nat. Protoc. 7, 374–393 (2012).
pubmed: 22301776
pmcid: 3724521
doi: 10.1038/nprot.2011.446
O’Rourke, K. P., Ackerman, S., Dow, L. E. & Lowe, S. W. Isolation, culture, and maintenance of mouse intestinal stem cells. Bio Protoc. 6, e1733 (2016).
pubmed: 27570799
Huch, M. et al. Unlimited in vitro expansion of adult bi-potent pancreas progenitors through the Lgr5/R-spondin axis. EMBO J. 32, 2708–2721 (2013).
pubmed: 24045232
pmcid: 3801438
doi: 10.1038/emboj.2013.204
Zafra, M. P. et al. An in vivo Kras allelic series reveals distinct phenotypes of common ocogenic variants. Cancer Discov. 10, 1654–1671 (2020).
Amen, A. M. et al. Endogenous spacing enables co-processing of microRNAs and efficient combinatorial RNAi. Cell Rep. Methods 2, 100239 (2022).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
pubmed: 23104886
doi: 10.1093/bioinformatics/bts635
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
pubmed: 25516281
pmcid: 4302049
doi: 10.1186/s13059-014-0550-8
Finn, J. D. et al. A single administration of CRISPR/Cas9 lipid nanoparticles achieves robust and persistent in vivo genome editing. Cell Rep. 22, 2227–2235 (2018).
pubmed: 29490262
doi: 10.1016/j.celrep.2018.02.014
Paffenholz Stella, V. et al. Senescence induction dictates response to chemo- and immunotherapy in preclinical models of ovarian cancer. Proc. Natl Acad. Sci. USA 119, e2117754119 (2022).
pubmed: 35082152
pmcid: 8812522
doi: 10.1073/pnas.2117754119
Leibold, J. et al. Somatic tissue engineering in mouse models reveals an actionable role for WNT pathway alterations in prostate cancer metastasis. Cancer Discov. 10, 1038–1057 (2020).
pubmed: 32376773
pmcid: 7334089
doi: 10.1158/2159-8290.CD-19-1242